Analysis device, recording medium, and analysis method

ABSTRACT

To analyze data obtained from a series of sports plays as a series or a set, an analysis device is provided, including: a process configured to implement an acquisition function of acquiring data indicating play events defined based on a motion of a user who plays a sport, an extraction function of extracting a plurality of play events classified into the same type among the play events, and an analysis function of analyzing data indicating the plurality of extracted play events.

TECHNICAL FIELD

The present disclosure relates to an analysis device, a recordingmedium, and an analysis method.

BACKGROUND ART

Techniques of assisting with a sports play using sensing or analysishave already been developed. For example, Patent Literature 1 disclosesa technique of detecting a swing motion using detection data of a motionsensor, extracting data in which a swing motion is detected as swingcandidate data, and selecting true swing data from swing candidate databased on a determination condition associated with a swing. Thus, forexample, the user need not support a start timing and an end timing of aswing motion, and it is possible to extract swing data with a relativelysmall computational load.

CITATION LIST Patent Literature

Patent Literature 1: JP 2012-254205A

SUMMARY OF INVENTION Technical Problem

However, in the technique disclosed in Patent Literature 1, it ispossible to analyze an individual swing, but acquired data is notanalyzed as a series or a set. In order to improve in a sports play, itis important to analyze an individual play such as a swing and findpoints for improvement, but, for example, a series of plays configuringa game or a set are interrelated, and even if an individual play isfocused on, influence of plays before and after it is unignorable.Further, it is difficult to understand a game or a combination of playsunless data acquired from a series of plays is analyzed as a series or aset.

In this regard, the present disclosure proposes an analysis device, arecording medium, and an analysis method, which are novel and improvedand capable of analyzing data obtained from a series of sports plays asa series or a set.

Solution to Problem

According to the present disclosure, there is provided an analysisdevice, including: a process configured to implement an acquisitionfunction of acquiring data indicating play events defined based on amotion of a user who plays a sport, an extraction function of extractinga plurality of play events classified into the same type among the playevents, and an analysis function of analyzing data indicating theplurality of extracted play events.

According to the present disclosure, there is provided a recordingmedium having a program stored therein, the program causing a computerto implement: an acquisition function of acquiring data indicating playevents defined based on a motion of a user who plays a sport; anextraction function of extracting a plurality of play events classifiedinto the same type among the play events; and an analysis function ofanalyzing data indicating the plurality of extracted play events.

According to the present disclosure, there is provided an analysismethod, including: acquiring data indicating play events defined basedon a motion of a user who plays a sport; extracting a plurality of playevents classified into the same type among the play events; andanalyzing data indicating the plurality of extracted play events.

Advantageous Effects of Invention

As described above, according to the present disclosure, it is possibleto analyze data obtained from a series of sports plays as a series or aset.

Note that the effects described above are not necessarily limited, andalong with or instead of the effects, any effect that is desired to beintroduced in the present specification or other effects that can beexpected from the present specification may be exhibited.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an example of a system configurationaccording to an embodiment of the present disclosure.

FIG. 2 is a block diagram schematically illustrating a deviceconfiguration of a system according to an embodiment of the presentdisclosure.

FIG. 3 is a diagram for describing an example of an analysis processaccording to an embodiment of the present disclosure.

FIG. 4 is a diagram conceptually illustrating a function of a filtersection in the example of FIG. 3.

FIG. 5 is a diagram illustrating an example of analysis of play eventgroups extracted in the example of FIG. 4.

FIG. 6 is a diagram for describing a relation between a distribution offeatures of play events and a learning level illustrated in the exampleof FIG. 5.

FIG. 7 is a diagram illustrating another example of analysis of featuresof play events in the example of FIG. 4.

FIG. 8 is a diagram illustrating an additional example of an analysisresult of play events of a single user according to the presentembodiment.

FIG. 9 is a diagram illustrating an additional example of an analysisresult of play events of a single user according to the presentembodiment.

FIG. 10 is a diagram for describing an example of analysis of playevents of a plurality of users according to an embodiment of the presentdisclosure.

FIG. 11 is a diagram for specifically describing an example of theanalysis of the play events of a plurality of users according to anembodiment of the present disclosure.

FIG. 12 is a diagram illustrating an example of information in which ananalysis result illustrated in FIG. 11 is expressed on a 2D plane.

FIG. 13 is a diagram illustrating an example of information in which ananalysis result illustrated in FIG. 11 is expressed on a 2D plane.

FIG. 14 is a diagram illustrating an additional example of an analysisresult of play events of a plurality of users according to the presentembodiment.

FIG. 15 is a diagram illustrating an additional example of an analysisresult of play events of a plurality of users according to the presentembodiment.

FIG. 16 is a diagram illustrating an example of a hardware configurationof a sensor apparatus according to an embodiment of the presentdisclosure.

FIG. 17 is a diagram illustrating an example of a hardware configurationof an analysis apparatus according to an embodiment of the presentdisclosure.

DESCRIPTION OF EMBODIMENTS

Hereinafter, (a) preferred embodiment(s) of the present disclosure willbe described in detail with reference to the appended drawings. In thisspecification and the drawings, elements that have substantially thesame function and structure are denoted with the same reference signs,and repeated explanation is omitted.

The description will proceed in the following order.

1. System configuration

2. Example of analysis process

2-1. Analysis of play events of single user

2-2. Analysis of play events of plurality of users

3. Hardware configurations

4. Supplement

The following description will proceed with a specific example of sports(tennis), but an application range of the present technology is notlimited to the sports described below. For example, the presenttechnology can be applied to any sport as long as a play event isdefined based on a motion of the user who plays the sport.

(1. System Configuration)

FIG. 1 is a figure which shows an example of a system configurationaccording to an embodiment of the present disclosure. With reference toFIG. 1, the system 10 includes a sensor apparatus 100, a smart phone200, and a server 300.

The sensor apparatus 100 is mounted in a tennis racket R. The sensorapparatus 100 includes, for example, a motion sensor (for example, anacceleration sensor, a gyro sensor, a geomagnetic sensor, or the like).In this case, the sensor apparatus 100 directly detects a motion of theracket R, but since the racket R is gripped by the user and movesaccording to the user's intention, it can be said that the sensorapparatus 100 detects the motion of the user indirectly through themotion of the racket R. In the present disclosure, in this case, it canbe said that the sensor apparatus 100 is indirectly mounted on the userand detects the motion of the user.

In another embodiment, the sensor apparatus 100 may be mounted, forexample, on clothing or a shoe of the user. In this case, the sensorapparatus 100 directly detects a motion of the clothing or the shoe, butsince the clothing or the shoe moves with the user, it can be said thatthe sensor apparatus indirectly detects the motion of the user.Alternatively, the sensor apparatus 100 may be directly mounted on theuser and, for example, may be put around an arm in a band form. In thiscase, the sensor apparatus 100 directly detects the motion of the user.In addition to when the sensor apparatus 100 directly detects the motionof the user, even when the sensor apparatus 100 indirectly detects themotion of the user, it is possible to define a play event correspondingto the motion of the user who plays a sport based on a detection resultprovided by the sensor apparatus 100 as long as the motion of the useris reflected in the detected motion.

The sensor apparatus 100 may further include a vibration sensor. Forexample, intervals (for example, intervals before and after an impact ona ball) corresponding to a play event can be easily specified based ondata detected by the vibration sensor. Further, the data detected by thevibration sensor may be used for analysis of a play event as well,similarly to the data detected by the motion sensor. The sensorapparatus 100 may further include a sensor that acquires environmentalinformation of the user who plays a sport such as a temperature,moisture, brightness, or a position. The data detected by various kindsof sensors with which the sensor apparatus 100 is equipped ispreprocessed as necessary and then transmitted to the smart phone 200through wireless communication such as Bluetooth (a registeredtrademark).

For example, the smart phone 200 is arranged near the user who isplaying a sport. In this case, the smart phone 200 receives the datatransmitted from the sensor apparatus 100 through wireless communicationsuch as Bluetooth (a registered trademark), temporarily accumulates orprocesses the received data as necessary, and transmits the resultingdata to the server 300 through network communication. The smart phone200 may receive a result of analysis performed by the server 300 basedon the transmitted data and output the analysis result to the userthrough a display, a speaker, or the like. The analysis result may beoutput when the user is not playing a sport. The output of the analysisresult may be performed by an information processing terminal used bythe user such as a personal computer or a tablet terminal, a gamemachine, a television, or the like, separately from the smart phone 200.

The smart phone 200 may not necessarily be arranged near the user who isplaying a sport. In this case, the sensor apparatus 100 accumulates thedetected data in an internal storage region (a memory or an externalstorage device). For example, the data may be transmitted from thesensor apparatus 100 to the smart phone 200 through wirelesscommunication such as Bluetooth (a registered trademark) when the sensorapparatus 100 and the smart phone 200 approach each other after thesports play. Alternatively, the data may be transmitted when the sensorapparatus 100 is connected with the smart phone 200 in a wired mannersuch as USB after the sports play. Further, a removable recording mediummay be used for the data transfer from the sensor apparatus 100 to thesmart phone 200.

The server 300 communicates with the smart phone 200 via network, andreceives the data detected by various kinds of sensors with which thesensor apparatus 100 is equipped. The server 300 performs an analysisprocess using the received data, and generates various informationrelated to a sports play. For example, the server 300 defines a playevent based on data that directly or indirectly indicates the motion ofthe user who plays a sport and is acquired by the motion sensor. Forexample, the play event corresponds to a single shot using the racket R.By defining the play event, for example, it is possible to understandplays of the user indicated by motion data as a sequence of plays havinga meaning such as {serve, stroke, volley, . . . }.

In addition, the server 300 may extract a plurality of play eventsclassified into the same type among the play events through the analysisprocess for the play event and analyze data indicating a plurality ofextracted play events. For example, information generated by theanalysis process of the server 300 is transmitted to the smart phone 200and output toward the user through the display or the speaker of thesmart phone 200. Alternatively, the server 300 may transmit theinformation to an information processing terminal other than the smartphone 200 and output the information toward the user. The server 300 mayperform the analysis process based on data received for each of aplurality of users, generate information based on a result of comparing,for example, play patterns generated for each user, and transmit thegenerated information to the information processing terminal of eachuser.

FIG. 2 is a block diagram schematically illustrating a deviceconfiguration of a system according to an embodiment of the presentdisclosure. Referring to FIG. 2, the sensor apparatus 100 includes asensor 110, a processing section 120, and a transmission section 130.The smart phone 200 includes a reception section 210, a processingsection 220, a storage section 230, a transmission section 240, animaging section 250, an input section 260, and an output section 270.The server 300 includes a reception section 310, a processing section320, a storage section 330, and a transmission section 340. Hardwareconfiguration examples (hardware configuration examples of the sensorapparatus and the analysis device and the analysis device) forimplementing the respective devices will be described later.

In the sensor apparatus 100, the processing section 120 processes thedata acquired by the sensor 110, and the transmission section 130transmits the processed data to the smart phone 200. The sensor 110includes, for example, the motion sensor that directly or indirectlydetects the motion of the user who plays the sport as described above.The sensor 110 may further include the vibration sensor, a sensor foracquiring the environmental information of the user, or the like. Theprocessing section 120 is implemented by a processor that operatesaccording to a program, and performs preprocessing on the data acquiredby the sensor 110 as necessary. The preprocessing may include, forexample, sampling, noise reduction, or the like. The preprocessing maynot necessarily be performed. The transmission section 130 isimplemented by a communication device, and transmits the data to thesmart phone 200, for example, using wireless communication such asBluetooth (a registered trademark). Although not illustrated in FIG. 2,the sensor apparatus 100 may include a storage section that temporarilyaccumulates data.

In the smart phone 200, the reception section 210 receives the datatransmitted by the sensor apparatus 100, and the transmission section240 transmits data to the server 300. The reception section 210 and thetransmission section 240 are implemented by a communication device thatperforms, for example, wireless communication such as Bluetooth (aregistered trademark) and wired or wireless network communication. Thereceived data is temporarily stored in the storage section 230 and thentransmitted, for example, through the processing section 220. Theprocessing section 220 may perform preprocessing on the received data.The processing section 220 is implemented by a processor that operatesaccording to a program, and the storage section 230 is implemented by amemory or a storage. The reception section 210 may further receiveinformation transmitted from the server 300. For example, the receivedinformation may be output toward the user from the output section 270according to control of the processing section 220. The output section270 includes, for example, a display or a speaker.

Further, in the smart phone 200, the imaging section 250 acquires animage. For example, the imaging section 250 is implemented by a cameramodule in which an imaging element is combined with an optical systemsuch as a lens. The image may include the user who plays a sport as asubject. For example, the image acquired by the imaging section 250 istransmitted from the transmission section 240 to the server 300 togetherwith the data received through the reception section 210. For example,the server 300 may use the image for the analysis process together withthe data acquired by the sensor apparatus 100 or may embed the image ininformation generated by the analysis process. The input section 260includes, for example, a touch panel, a hardware button, a microphonethat receives an audio input, and/or a camera that receives a gestureinput. The processing section 220 may request the server 300 to transmitinformation through the transmission section 240 according to a useroperation acquired through the input section 260.

The server 300 includes a reception section 310, a processing section320, a storage section 330, and a transmission section 340. Thereception section 310 is implemented by a communication apparatus, andreceives data transmitted by using network communication such as theinternet from the smart phone 200. The processing section 320 isimplemented, for example, by a processor such as a CPU, and processesthe received data. For example, the processing section 320 executes ananalysis process of the process of the received data, and mayadditionally accumulate data after analysis in the storage section 330,or may output the data via the transmission section 340. Alternatively,the processing section 320 may only execute a control of theaccumulation or output of the data already analyzed in the smart phone200 or the like.

The configuration of the system according to an embodiment of thepresent disclosure has been described above. The above-describedconfiguration is an example, and various modifications can be made inother embodiments. For example, in the above example, the analysisprocess using the data acquired by the sensor apparatus 100 is performedby the processing section 320 of the server 300, but the analysisprocess may be performed by the processing section 220 of the smartphone 200 or the processing section 120 of the sensor apparatus 100. Thesystem 10 has been described as including the sensor apparatus 100, thesmart phone 200, and the server 300, but, for example, when theprocessing section 220 of the smart phone 200 performs the analysisprocess, the system 10 may not include the server 300. Alternatively, inthis case, the server 300 provides a service of storing the informationobtained by the analysis process and sharing the information with theuser. Further, for example, when the processing section 120 of thesensor apparatus 100 performs the analysis process, the system 10 maynot include the smart phone 200 and the server 300. The sensor apparatus100 may be, for example, a dedicated sensor apparatus mounted on theuser or a tool, or a sensor module mounted in a portable informationprocessing terminal may function as the sensor apparatus 100. Thus, thesensor apparatus 100 may be implemented in the same apparatus as thesmart phone 200.

(2. Example of Analysis Process)

FIG. 3 is a diagram for describing an example of the analysis processaccording to an embodiment of the present disclosure. Referring to FIG.3, the processor that performs the analysis process acquires motion data401 and metadata 403 indicating the play event defined based on themotion of the user who plays the sport as an input. Then, the processorextracts a plurality of play events classified into the same type amongthe play events indicated by the data through a function of a filtersection 405. Further, the processor extracts a feature of a motionindicated by the data corresponding to a plurality of extracted playevents through a function of a feature extraction section 407, andperforms determination based on the feature through a function of adetermination section 409.

Here, the motion data 401 is data acquired through the motion sensorarranged in the sensor 110 with which the sensor apparatus 100 isequipped, and indicates the motion of the user who plays the sport. Themetadata 403 is data indicating the play event defined based on themotion. In the illustrated example, the motion data 401 is associatedwith the metadata 403. The metadata 403 defines, for example, a type oran attribute of the play event indicated by the associated motion data401. For example, in the case of tennis, the type of play event mayinclude a type of shot such as a serve, a forehand stroke, a backhandstroke, a forehand volley, and the like. Further, in this case, theattribute of the play event may include information such as anoccurrence time (for example, a time at which a racket impacts a ball),a swing speed, and the like.

The motion data 401 and the metadata 403 are an example of dataindicating the play event, and data indicating the play event may beacquired in various other forms. For example, an individual play eventmay be indicated by a single piece of data in which the motion data iscombined with the metadata, and motion data indicating a plurality ofplay events which occur consecutively may be integrated into one, anddifferent intervals of the motion data may be referred to by a pluralityof pieces of metadata.

As described above, the filter section 405 extracts a plurality of playevents classified into the same type from among the play eventsindicated by the motion data 401 and the metadata 403. As describedabove, for example, in the case of tennis, the type of play event mayinclude a type of shot such as a serve, a forehand stroke, a backhandstroke, a forehand volley, and the like. In this regard, the filtersection 405 extracts the play event of the type of any one shot. Asingle play event may be extracted. However, in this case, the analysisprocess performed on the extracted play event may differ from processesof the feature extraction section 407 and the determination section 409that will be described later.

The feature extraction section 407 performs the analysis process ofextracting the feature on the data indicating a plurality of play eventsextracted through the filter section 405, that is, the datacorresponding to a plurality of play events extracted through the filtersection 405 among the motion data 401 and the metadata 403. The featureextracted from the data of the play events by the feature extractionsection 407 may differ, for example, according to the analysis processby the determination section 409 arranged at the subsequent stagethereto. The extracted feature may differ in the type of play eventextracted by the filter section 405. For example, k-means coding, anauto-encoder, or the like may be used as a method of extracting afeature. Since these methods are already known, a detailed descriptionthereof is omitted herein.

The determination section 409 performs some sort of determination on aplurality of play events extracted by the filter section 405 based onthe feature extracted through the feature extraction section 407. Forexample, when a plurality of extracted play events are the play eventsof a single user, the determination section 409 may determine a learninglevel of the play indicated by the play events. More specifically, thedetermination section 409 may evaluate a degree of stability of the playbased on the extracted feature and determine the learning level based onthe degree of stability. Alternatively, the determination section 409may determine the learning level using a learning level determinationdevice in which learning is performed based on training data of eachlearning level (an advanced level, an intermediate level, and a beginnerlevel) collected for each type of play event (for example, a type ofswing) in advance.

Further, for example, when a plurality of extracted play events includethe play events of a plurality of users, the determination section 409may determine a degree of similarity of the plays of the users indicatedby the play events. More specifically, the determination section 409 maycalculate distances of the plays of the users in a feature space basedon the extracted feature (similar to evaluation of the degree ofstability) and determine the degree of similarity based on the distance.

The processor that performs the analysis process may further implement afunction of causing information generated in the analysis to bedisplayed on a display (which is arranged in the output section 270 ofthe smart phone 200, for example). In this case, for example,information indicating the feature extracted through the featureextraction section 407 may be displayed on the display. Several examplesof such a display will be described later.

The analysis process performed by the feature extraction section 407 andthe determination section 409 in the above example may be replaced withan analysis process according to any other method, for example, ananalysis process that does not depend on feature extraction. Forexample, when a motion of the user in a certain type of play event isindicated as a waveform of acceleration or the like by the motion data401, the degree of stability or the degree of similarity may bedetermined based on a distance between waveforms of a predeterminednumber of sample intervals. Further, in the analysis process, forexample, a habit in the play of the user may be detected.

FIG. 4 is a diagram conceptually illustrating the function of the filtersection in the example of FIG. 3. FIG. 4 illustrates an example in whichplay event groups 1103 of the same type are extracted from a time seriesconfigured with three types of play events 1101. More specifically, theplay events 1101 include a play event 1101 a of a forehand stroke(FHST), a play event 1011 b of a backhand stroke (BHST), and a playevent 1101 c of a serve (SRV). The play event groups 1103 include a playevent group 1103 a of a forehand stroke, a play event group 1103 b of abackhand stroke, and a play event group 1103 c of a serve.

For example, the filter section 405 extracts the play event groups 1103a to 1103 c from the time series of the play events 1101. The play eventgroups 1103 a to 1103 c may be extracted in parallel at the same time,the analysis process for each play event group may be performed by thefeature extraction section 407 and the determination section 409, andanalysis results of a plurality of play event types may be output at thesame time. Alternatively, some of the play event groups 1103 a to 1103 cmay be extracted, and the analysis process for the extracted play eventgroups may be performed by the feature extraction section 407 and thedetermination section 409.

In the illustrated example, the play event groups 1103 are extractedfrom the play events of a single user 1101, but the same applies evenwhen the play event groups are extracted from the play events of aplurality of users. In this case, for example, metadata such as a userID indicating the user who performs the play event may be added to thedata of the play events included in the play event group. An example inwhich the play event groups are extracted from the play events of asingle user will be described with reference to FIGS. 5 to 7. An examplein which the play event groups are extracted from the play events of aplurality of users will be described later.

(2-1. Analysis of Play Events of Single User)

FIG. 5 is a diagram illustrating an example of analysis of the playevent groups extracted in the example of FIG. 4. Referring to FIG. 5,for each of the play event groups 1103 a to 1103 c extracted by thefilter section 405, the feature extraction section 407 extracts thefeatures of the respective play events, and the determination section409 evaluates a degree of stability based on a distribution of thefeatures of the respective play events.

Here, the features of the respective play events are expressed, forexample, as multi-dimensional feature vectors. In FIG. 5, thedistribution of the feature vectors is expressed on a two-dimensional(2D) plane. For example, when a function of causing the informationgenerated in the analysis to be displayed on the display is implementedby the processor that performs the analysis process, informationindicating a distribution of the respective play events in a featurespace on the 2D plane may be displayed. A technique of enabling themulti-dimensional feature vectors to be viewed on the 2D plane or athree-dimensional (3D) space is already well known, and thus a detaileddescription thereof is omitted herein.

In the illustrated example, the distribution of the features of therespective play events greatly differs according to the play event group1103. Thus, the degree of stability evaluated based on the distributiongreatly differs according to the play event group 1103. As will bedescribed with reference to the next drawing, there are cases in whichthe degree of stability is used as an index indicating the learninglevel of the play. Thus, in the illustrated example, the learning levelof the play can be determined for each play event group 1103, that is,for each type of play event. If the learning level is assumed toincrease as the degree of stability increases, the user in the exampleof FIG. 5 is estimated to be relatively high in the learning level forthe forehand stroke, relatively low in the learning level for thebackhand stroke, and intermediate in the learning level for the serve.As described above, the fact that information of each learning level isgenerated for each type of play event, for example, for each type ofshot in the case of tennis, is one of the advantages of the presentembodiment.

FIG. 6 is a diagram for describing a relation between a distribution ofthe features of the play events and the learning level illustrated inthe example of FIG. 5. Referring to FIG. 6, for two play event groups1103 a 1 and 1103 a 2 of a forehand stroke, the degree of stability isevaluated based on a distribution. Here, the play event group 1103 a 1is a play event group of a forehand stroke of an advanced-level tennisplayer, and the play event group 1103 a 2 is a play event group of aforehand stroke of a beginner-level tennis player.

According to a hypothesis formed by the inventor(s) of the presenttechnology, the distribution of the features of the play events includedin the play event group of the same type of shots decreases as thelearning level increases. Thus, the distribution of the respective playevents in the feature space included in the play event group 1103 a 1 ofthe advanced-level player is smaller than the distribution of therespective play events in the feature space included in the play eventgroup 1103 a 2 of the beginner-level player. This is considered to bedue to the fact that the advanced-level player having the high learninglevel has little variation in a swing motion, and the same type of swingis performed in many cases.

Here, for example, when the play event is defined for a tennis shot, thefeature of the play event defined based on the motion of the user at thetime of a shot differs according to a type of shot. Thus, even when thefeature distribution of the play events is calculated in a state inwhich the play events (shots) of different types are mixed, a relationbetween the distribution and the degree of stability illustrated in FIG.6 and the learning level is not held. Thus, the fact that evaluation ofthe degree of stability based on the above features and determination ofthe learning level based on the degree of stability can be performed isalso one of the advantages of the present embodiment in which each ofthe play event groups obtained by classifying the play events into thesame type is analyzed.

FIG. 7 is a diagram illustrating another example of the analysis basedon the features of the play events in the example of FIG. 4. Referringto FIG. 7, regions A1 to A3 in which the features of the play events ofthe players (users) of various learning levels are distributed aredefined in the feature space. The region A1 is a region in which thefeatures of the beginner-level player are distributed, the region A2 isa region in which the features of the intermediate-level player aredistributed, and the region A3 is a region in which the features of theadvanced-level player are distributed.

The example illustrated in FIG. 7 corresponds to the case in which thedetermination section 409 determines the learning level using a learninglevel determination device in which learning is performed based ontraining data of each learning level (an advanced level, an intermediatelevel, and a beginner level) collected for each type of play event (forexample, a type of swing) in advance. In the illustrated example, thelearning level determination device is formed by learning using thedistribution of the features of the play events of the player of eachlearning level as the training data. For example, the expression of theregions on the 2D plane illustrated in FIG. 7 may also be used, forexample, when the processor that performs the analysis processimplements the function of causing the information generated in theanalysis to be displayed on the display.

Further, in the illustrated example, points P indicating a time-seriestransition of a feature distribution of a certain user are illustratedin the feature space. In this example, it is illustrated that the centerof the feature distribution of the user located at a point P1 in theregion A1 of the beginner level when a service is initially usedtransitions up to a point P2 in the region A2 of the intermediate levelwith the improvement, and is currently moving toward the region A3 ofthe advanced level and located at a point P3 (or in the region A2 of theintermediate level). An expression of such a time-series transition mayalso be used for a display of the information generated in the analysis.

FIGS. 8 and 9 are diagrams illustrating additional examples of theanalysis result of the play events of a single user according to thepresent embodiment. An expression of such an analysis result may also beused for a display of the information generated in the analysis.

In the example illustrated in FIG. 8, a one-day temporal change in thedegree of stability of the features of the play events described abovewith reference to FIGS. 5 and 6 is detected. In the illustrated example,for a certain day on which the user plays almost all day (about 10:00 to17:00), the degree of stability of the features of the play events isexpressed hourly by a polygonal line extending from a start point Ps toan end point Pe using time as a horizontal axis. Through such anexpression, for example, information such as “time necessary for awarm-up” or “time taken until a play is influenced by fatigue” can beobtained from the one-day change in the degree of stability of the play.

Further, for example, when information related to the entire play of theuser is obtained as illustrated in FIG. 8, the degree of stability ofthe features calculated for each type of play event may be averaged foreach type of play event and output. As described above, in order tocalculate the significant degree of stability, it is effective toperform the analysis process for each type of play event, but after thedegree of stability is calculated, for example, an averaging processbeyond the type of play event may be performed.

In the example illustrated in FIG. 9, a relatively long-term temporalchange in the degree of stability of the features of the play eventsdescribed above with reference to FIGS. 5 and 6 over several months isdetected. In the illustrated example, the degree of stability of thefeatures of the play events is expressed monthly during a period ofJanuary to July using time as a horizontal axis. For example, a degreeof proficiency in the play over the past several months can beunderstood from the change in the degree of stability of the play over acertain period of time. When the degree of stability is expressed foreach type of play event (shot) as in the illustrated examples, theproficiencies in the plays corresponding to the respective types of playevents can be improved. Further, a similar expression can be used forcomparing the degrees of stability of the different users for the sametype of play event.

(2-2. Analysis of Play Events of Plurality of Users)

FIG. 10 is a diagram for describing an example of the analysis of theplay events of a plurality of users according to an embodiment of thepresent disclosure. Referring to FIG. 10, the processor that performsthe analysis process performs a comparison process 413 based on featuredata 411 extracted from the play events of a plurality of users by thefeature extraction section 407, and obtains an analysis result 415. Theanalysis result 415 may include, for example, a feature map of a play ofeach player described below, a similar play map, and the like.

In the comparison process 413, further, supplementary information 417provided to a first user whose feature data of the play events isincluded in the feature data 411 may be used. In this case, thesupplementary information 417 for the first user may be provided to asecond user (whose feature data of the play events is similarly includedin the feature data 411) whose play is indicated to be similar to thefirst user according to the result of the comparison process 413. Thesupplementary information 417 may include, for example, advice on acertain type of play (in the case of tennis, for example, a forehandstroke) of the user, a past opponent of the user, an impression thereof,and the like. The same supplementary information 417 as that for thefirst user is likely to be effective for the second user whose play isindicated to be similar to that of the first user by the feature data411.

FIG. 11 is a diagram for specifically describing an example of theanalysis of the play events of a plurality of users according to anembodiment of the present disclosure. Referring to FIG. 11, theprocessor that performs the analysis process issues a query 419, andperforms an extraction process 423 of extracting data from motion data421. An analysis result 427 is obtained from the extracted data throughan analysis process 425. Further, a display 431 of the analysis resultis implemented based on the analysis result 427 through a displayprocess 429.

In the query 419, for example, the type of play event to be extracted(for example, a type of shot such as a forehand stroke, a backhandstroke, a serve, or the like in the case of tennis) and the user of anextraction target are designated. For example, the motion data 421 ofmany users is stored in a database of the storage section 330 of theserver 300, but the number of users for whom the analysis of the playevents in the present embodiment, for example, the determination of thedegree of similarity of the play, can be effectively performed islimited, and thus the user of the target may be limited. Further, sincethe motion data is also a sort of personal information, the users of theextraction target may be limited by limiting a publication target (up tofriends or the like).

In the analysis process 425, the processes of the feature extractionsection 407 and the determination section 409 are performed. In otherwords, the features of the play events are extracted from the extractedmotion data (which is limited to a specific type of play event by thequery 419), and the determination of the degree of similarity or thelike is performed based on the extracted features. For example, when thedistances of the plays of a plurality of players (users) in the featurespace are calculated in the analysis process 425, the process isperformed by the following program:

for i=1,...,N  for j=i+1,...,N  (calculate average distance betweenfeature of player i and  feature of player j)  (substitute calculateddistance into C(i,j))  end end

In the above case, the analysis result 427 indicating the distancebetween the plays of the respective players in the feature space mayinclude a matrix C. As described in the above program, in the matrix C,an element C(i,j) indicates a distance between a play of an i-th playerand a play of a j-th player in the feature space. Further, in this case,in the display process 429, the distance between the plays of therespective players in the feature space expressed by the matrix C isexpressed on the 2D plane through a process of solving an optimizationproblem illustrated in the following Formula 1.

$\begin{matrix}{\left\lbrack {{Math}.\mspace{14mu} 1} \right\rbrack \mspace{545mu}} & \; \\{\min\limits_{x}{\sum\limits_{i < j}\; \left( {{{x_{i} - x_{j}}} - {C\left( {i,j} \right)}} \right)^{2}}} & \left( {{Formula}\mspace{14mu} 1} \right)\end{matrix}$

FIGS. 12 and 13 are diagrams illustrating examples of information forexpressing the analysis result illustrated in FIG. 11 on the 2D plane.In the illustrated example, when a plurality of users actually playtennis, triaxial acceleration and data (motion data) of an angularvelocity were acquired using the sensor apparatus 100 mounted on theracket, and a positional relation between the features extracted fromthe motion data of the user in the feature space was expressed on the 2Dplane. FIG. 12 illustrates the features of the play events in which thetype (the swing type) is the forehand stroke, and FIG. 13 illustratesthe features of the play events in which the type is the serve. Forexample, expressions of regions on the 2D plane illustrated in FIGS. 12and 13 may also be used, for example, when the processor that performsthe analysis process implements the function of causing the informationgenerated in the analysis to be displayed on the display.

In the above example, in FIGS. 12 and 13, the features of female players(users) show a tendency to be distributed in a concentrated region Aw onthe 2D plane. Things specifically indicated by this tendency can bevariously inferred. By analyzing the features for each type of playevent based on this result, a certain tendency is estimated to be easilyshown in the analysis result.

FIGS. 14 and 15 are diagrams illustrating additional examples of theanalysis result of the play events of a plurality of users according tothe present embodiment. An expression of the analysis result of such anexample may also be used for the display of the information generated inthe analysis.

In the example illustrated in FIG. 14, among other users (who may be,for example, professional players) serving as comparison targets, a userwhose play is similar to a play (serve) indicated by a play event group1103 c 1 for a serve of a certain user is determined. In this case, thefeatures of the play events included in the play event group 1103 c 1 ofthe user are compared with the features of the play events included ineach of play event groups 1103 c 2 to 1103 c 4 of the other users, andthe degree of similarity is calculated for each of the play event groups1103 c 2 to 1103 c 4. In the illustrated example, since the degree ofsimilarity of the play event group 1103 c 3 is highest, an analysisresult 1105 indicating that the user is the same type as the user of theplay event group 1103 c 3 is output.

In the example illustrated in FIG. 15, for the play (serve) indicated bythe play event group 1103 c for the serve, the features of the playevents of the respective users are mapped on the 2D plane, and ananalysis result 1107 displayed together with icons indicating therespective users is output. The analysis result 1107 is similar to, forexample, the map described above with reference to FIGS. 12 and 13, but,for example, since the analysis result 1107 is displayed together withthe icons, it is easily understood that it is a degree-of-similarity mapfor the plays of the respective users.

(3. Hardware Configurations)

Next, examples of hardware configurations for implementing the sensorapparatus and the analysis apparatus (in the above described examples,the sensor apparatus, the smart phone, or the server) according to anembodiment of the present disclosure will be described with reference toFIGS. 16 and 17.

(Sensor Apparatus)

FIG. 16 is a diagram illustrating an example of a hardware configurationof the sensor apparatus according to an embodiment of the presentdisclosure. With reference to FIG. 16, the sensor apparatus 100 mayinclude a sensor 101, a Central Processing Unit (CPU) 103, a Read OnlyMemory (ROM) 105, a Random Access Memory (RAM) 107, a user interface109, an external storage apparatus 111, a communication apparatus 113,and an output apparatus 115. These elements are mutually connected by abus, for example.

For example, the sensor 101 includes an acceleration sensor, an angularvelocity sensor, a vibration sensor, a magnetic field sensor, atemperature sensor, a pressure sensor (including a press switch), aGlobal Positioning System (GPS) receiver or the like. The sensor 101 mayinclude a camera (imaging sensor) or a microphone (audio sensor).

The CPU 103, the ROM 105 and the RAM 107 implement various types offunctions with software, by reading and executing program instructions,for example, recorded in the external storage apparatus 111. In theembodiments of the present disclosure, functions such as control of theentire sensor apparatus 100 may be implemented, for example, by the CPU103, the ROM 105 and the RAM 107.

The user interface 109 is, for example, an input apparatus such asbuttons or a touch panel, which receives user operations of the sensorapparatus 100. For example, operations of a user may instruct the startor completion of the transmission of sensor information from the sensorapparatus.

The external storage apparatus 111 stores various types of informationrelated to the sensor apparatus 100. For example, program instructionsfor causing functions to be implemented by software in the CPU 103, theROM 105 and RAM 107 may be stored in the external storage apparatus 111,or data acquired by the sensor 101 may be cached temporarily. Whenconsidering that the sensor apparatus 100 is mounted in a hitting toolor the like, it is desirable to use a sensor apparatus, for example,with a strong impact such as a semiconductor memory, as the externalstorage apparatus 111.

Further, a configuration corresponding to an internal storage region (amemory or an external storage device) that accumulates data detected inthe sensor apparatus 100 when the smart phone 200 is not arranged nearthe user who is playing a sport is the ROM 105, the RAM 107, and/or theexternal storage apparatus 111.

The communication apparatus 113 communicates with the analysis apparatus600, which will be described later, by various types of wired orwireless communication systems. Further, the communication apparatus 113may directly communicate with the analysis apparatus 600 by inter-devicecommunication, or may communicate with the analysis apparatus 600 via anetwork such as the internet.

The output apparatus 115 is constituted by an apparatus capable ofoutputting information as light, audio or images. For example, theoutput apparatus 115 may output information which notifies detection ofa time or play event in the sensor apparatus 100, or may output a visualor aural notification to a user, based on an analysis result receivedfrom the analysis apparatus 600 or an analysis result calculated in thesensor apparatus 100. For example, the output apparatus 115 includes,for example, a display such as a lamp of an LED or the like or an LCD, aspeaker, a vibrator or the like.

(Analysis Apparatus)

FIG. 17 is a diagram illustrating an example of a hardware configurationof the analysis apparatus according to an embodiment of the presentdisclosure. The analysis apparatus 600 may implement, for example, theanalysis apparatus according to an embodiment of the present disclosure,or the smart phone 200 or the server 300 described above. Note that, asdescribed above, the analysis apparatus may be implemented by the sensorapparatus 100.

The analysis apparatus 600 may include a CPU 601, a ROM 603, a RAM 605,a user interface 609, an external storage apparatus 611, a communicationapparatus 613, and an output apparatus 615. These elements are mutuallyconnected by a bus, for example.

The CPU 601, the ROM 603 and the RAM 605 implement various types offunctions with software, by reading and executing program instructions,for example, recorded in the external storage apparatus 611. In theembodiments of the present disclosure, control of the entire analysisapparatus 600, functions of the processing section in the abovedescribed functional configuration or the like, may be implemented, forexample, by the CPU 601, the ROM 603 and the RAM 605.

The user interface 609 is, for example, an input apparatus such asbuttons or a touch panel, which receives user operations of the analysisapparatus 600.

The external storage apparatus 611 stores various types of informationrelated to the analysis apparatus 600. For example, program instructionsfor causing functions to be implemented by software in the CPU 601, theROM 603 and RAM 605 may be stored in the external storage apparatus 611,or sensor information received by the communication apparatus 613 may becached temporarily. Further, a log of analysis results may beaccumulated in the external storage apparatus 611.

The output apparatus 615 is constituted by an apparatus capable ofvisually or aurally notifying information to a user. For example, theoutput apparatus 615 may be a display device such as a Liquid CrystalDisplay (LCD), or an audio output device such as a speaker orheadphones. The output apparatus 615 outputs a result obtained by theprocesses of the analysis apparatus 600 as video images such as text orpictures, or outputs the results as audio such as voices or sounds.

Heretofore, examples of the hardware configurations of the sensorapparatus 100 and the analysis apparatus 600 have been shown. Each ofthe above described constituent elements may be constituted by usinggeneric members, or may be constituted by hardware specialized for thefunctions of each of the constituent elements. Such a configuration maybe appropriately changed in accordance with the technology level at thetime of implementation.

(4. Supplement)

For example, the embodiments of the present disclosure may include ananalysis apparatus such as that described above (an informationprocessing terminal such as a smart phone, a server, or a sensorapparatus), a system, an information processing method executed by theanalysis apparatus or the system, a program for causing the analysisapparatus to function, and a non-temporarily tangible medium on whichprograms are recorded.

The preferred embodiment(s) of the present disclosure has/have beendescribed above with reference to the accompanying drawings, whilst thepresent disclosure is not limited to the above examples. A personskilled in the art may find various alterations and modifications withinthe scope of the appended claims, and it should be understood that theywill naturally come under the technical scope of the present disclosure.

In addition, the effects described in the present specification aremerely illustrative and demonstrative, and not limitative. In otherwords, the technology according to the present disclosure can exhibitother effects that are evident to those skilled in the art along with orinstead of the effects based on the present specification.

Additionally, the present technology may also be configured as below.

(1)

An analysis device, including:

a process configured to implement

an acquisition function of acquiring data indicating play events definedbased on a motion of a user who plays a sport,

an extraction function of extracting a plurality of play eventsclassified into the same type among the play events, and

an analysis function of analyzing data indicating the plurality ofextracted play events.

(2)

The analysis device according to (1),

wherein the analysis function analyzes a feature of the motioncorresponding to the plurality of extracted play events.

(3)

The analysis device according to (2),

wherein the plurality of extracted play events are the play events of asingle user, and

wherein the analysis function evaluates a play of the single userindicated by the plurality of extracted play events based on thefeature.

(4)

The analysis device according to (3),

wherein the analysis function evaluates a degree of stability of theplay of the single user based on the feature.

(5)

The analysis device according to (4),

wherein the analysis function determines a learning level of the play ofthe single user based on the degree of stability.

(6)

The analysis device according to any one of (3) to (5),

wherein the analysis function detects a temporal change in the feature.

(7)

The analysis device according to (2),

wherein the plurality of extracted play events include the play eventsof a plurality of users, and

wherein the analysis function compares plays of the plurality of usersindicated by the plurality of extracted play events based on thefeature.

(8)

The analysis device according to (7),

wherein the plurality of users include a first user and a second user,and

wherein the analysis function provides information that is provided tothe first user in connection with the play also to the second user whoseplay is indicated to be similar to the play of the first user accordingto a result of the comparing.

(9)

The analysis device according to any one of (1) to (8),

wherein the processor further implements a display control function ofcausing information generated in the analysis to be displayed on adisplay.

(10)

The analysis device according to (9),

wherein the analysis function analyzes a feature of the motioncorresponding to the plurality of extracted play events, and

wherein the display control function causes information indicating thefeature to be displayed.

(11)

The analysis device according to (10),

wherein the plurality of extracted play events include the play eventsof a plurality of users, and

wherein the display control function causes information indicating thefeatures of the plurality of users on a two-dimensional (2D) plane to bedisplayed.

(12)

A recording medium having a program stored therein, the program causinga computer to implement:

an acquisition function of acquiring data indicating play events definedbased on a motion of a user who plays a sport;

an extraction function of extracting a plurality of play eventsclassified into the same type among the play events; and

an analysis function of analyzing data indicating the plurality ofextracted play events.

(13)

An analysis method, including:

acquiring data indicating play events defined based on a motion of auser who plays a sport;

extracting a plurality of play events classified into the same typeamong the play events; and

analyzing data indicating the plurality of extracted play events.

REFERENCE SIGNS LIST

-   10 system-   100 sensor apparatus-   110 sensor-   120 processing section-   200 smart phone-   210 reception section-   220 processing section-   300 server-   310 reception section-   320 processing section-   401 metadata-   403 motion data-   405 filter section-   407 feature extraction section-   409 determination section

1. An analysis device, comprising: a process configured to implement an acquisition function of acquiring data indicating play events defined based on a motion of a user who plays a sport, an extraction function of extracting a plurality of play events classified into the same type among the play events, and an analysis function of analyzing data indicating the plurality of extracted play events.
 2. The analysis device according to claim 1, wherein the analysis function analyzes a feature of the motion corresponding to the plurality of extracted play events.
 3. The analysis device according to claim 2, wherein the plurality of extracted play events are the play events of a single user, and wherein the analysis function evaluates a play of the single user indicated by the plurality of extracted play events based on the feature.
 4. The analysis device according to claim 3, wherein the analysis function evaluates a degree of stability of the play of the single user based on the feature.
 5. The analysis device according to claim 4, wherein the analysis function determines a learning level of the play of the single user based on the degree of stability.
 6. The analysis device according to claim 3, wherein the analysis function detects a temporal change in the feature.
 7. The analysis device according to claim 2, wherein the plurality of extracted play events include the play events of a plurality of users, and wherein the analysis function compares plays of the plurality of users indicated by the plurality of extracted play events based on the feature.
 8. The analysis device according to claim 7, wherein the plurality of users include a first user and a second user, and wherein the analysis function provides information that is provided to the first user in connection with the play also to the second user whose play is indicated to be similar to the play of the first user according to a result of the comparing.
 9. The analysis device according to claim 1, wherein the processor further implements a display control function of causing information generated in the analysis to be displayed on a display.
 10. The analysis device according to claim 9, wherein the analysis function analyzes a feature of the motion corresponding to the plurality of extracted play events, and wherein the display control function causes information indicating the feature to be displayed.
 11. The analysis device according to claim 10, wherein the plurality of extracted play events include the play events of a plurality of users, and wherein the display control function causes information indicating the features of the plurality of users on a two-dimensional (2D) plane to be displayed.
 12. A recording medium having a program stored therein, the program causing a computer to implement: an acquisition function of acquiring data indicating play events defined based on a motion of a user who plays a sport; an extraction function of extracting a plurality of play events classified into the same type among the play events; and an analysis function of analyzing data indicating the plurality of extracted play events.
 13. An analysis method, comprising: acquiring data indicating play events defined based on a motion of a user who plays a sport; extracting a plurality of play events classified into the same type among the play events; and analyzing data indicating the plurality of extracted play events. 